Acoustic pulse reflectometry is the generic name given to a family of techniques used to measure the acoustic response of a given system. Its application to systems consisting of volumes of air bound by rigid surfaces is known. The term “APR” is derived from the fact that an excitation pulse (or “impulse”) is applied to the system, and the reflections created inside the system are then measured. The pulse need not be “real”, but may be in the form of pseudo-noise or frequency sweeps, see below. Various algorithms are applied to this acoustic response, in order to gain information regarding the system being examined.
Ideally, APR should enable extraction of the theoretical impulse response of the system being measured. In practice this is impossible, since an ideal pulse has an infinite bandwidth (BW), and therefore cannot be created under laboratory conditions. Normally a very short electrical pulse is applied to a transducer, producing an acoustic pulse of short duration, and as large a BW as possible. The transducer is coupled through a tube to the system or “object” being measured, with a microphone mounted in the tube wall. The microphone measures both the excitation pulse and the reflections from the object.
FIG. 1 shows a typical setup of a prior art APR system. A transducer emits an acoustic wave that propagates down two tubes, a left one with length L1 and a right one with length L2. The lengths L1 and L2 change according to the object being measured, and are typically between 3 and 6 meters. The wave is recorded as it propagates over the microphone. It then arrives at an object, creating reflections that propagate back down the tubes. These reflections are also recorded as they pass over the microphone. If the two tubes are sufficiently long, the right and left propagating waves do not overlap, and are recorded separately. From a purely experimental point of view, several technical problems are encountered in this type of setup:                1. The acoustic pulse typically has high amplitude, but is very short in duration. This results in a pulse having rather low energy content. This makes it difficult to obtain a high signal to noise ratio (SNR) in the reflections measured from the object.        2. The excitation pulse and the reflections from the object have finite duration, which can cause them to overlap at the microphone. This can make them very difficult to separate.        3. Reflections from the object eventually travel down to the transducer itself, reflect off it, and return to be measured once again by the microphone. These secondary reflections can once more interfere with measurements of the response of the object.        
Once the acoustic response has been measured, several types of analyses can be applied. In most cases, the first step is deconvolution of the reflected pulse and the excitation pulse [see e.g. N. Amir, G. Rosenhouse, U. Shimony, Acustica, Vol. 81, pages 450-462 and 463-474, 1995 (hereinafter “Amir1”)]. Deconvolution must be carried out, normally through division in the frequency domain or Singular Value Decomposition (SVD), because the excitation pulse rarely has a flat spectrum. Further analysis depends on the purpose of the measurements and the geometrical nature of the object being studied. Two typical problems arise according to two different applications:
Bore reconstruction: in this type of application, the system being examined is considered to be one-dimensional, i.e. it is much longer than its cross section, in the manner of a long tube, possibly having a varying cross section. Current methods assume that no transverse modes are excited in the tube, a fact that limits the usable bandwidth. This is somewhat in conflict with the objective of attaining an excitation pulse having the largest possible bandwidth, and some compromise must be reached. Once the impulse response of this kind of system is measured, various algorithms can be used to reconstruct the cross section of the tube—this is termed “bore reconstruction”. The most common algorithms are variants of the “layer-peeling algorithm” originally proposed by Ware and Aki in J. Acoust. Soc. Am., Vol. 45, pages 911-921, 1969. Other similar models include Amir1 above. The axial resolution of the reconstruction is determined by the bandwidth of the excitation pulse, whereas the accuracy in calculating the cross section is determined by the deconvolution process and the SNR. It is important to stress that as long as the cross section preserves the condition that no transverse modes are excited, it can be reconstructed with no other a-priori information.
Quality Assurance: in this type of application we wish to determine the conformity between an accurately measured prototype and a test object, such as components coming off a manufacturing line, or tubing in an aircraft being checked during routine maintenance. In such a case, acoustic measurements can be carried out on the prototype, with no particular constraints on its internal geometry. The acoustic signature of the prototype can then be compared to measurements taken from manufactured parts, in order to detect faults (leaks, internal deformations, blocked passageways etc.). This can be applied to various types of tubing, manifolds, cooling passageways in cast parts, etc. In the simplest case, any deviations from the prototypical measurements that fall out of predetermined limits can flag a fault. In the more general case, the measurements can be interpreted by automated algorithms, in order to determine the exact nature and location of faults.
Various APR systems and methods that attempted to solve some of the problems mentioned above are known and described for example in Japanese patents JP 7-55949, JP 7-71700, JP 7-198527, JP 7-198528, JP 11-125623 and patent applications (JP 2003-207329)
JP 7-55949 applies APR to find joints in a pipe. Both transmission (TX) and reception (RX) elements are at one end of pipe. Joints in the pipe create reflections that arrive earlier than the reflection from end of pipe. Peaks in the reflected signal are interpreted as joints, therefore this patent does not mention deconvolution of the reflections with the excitation signal. This would probably result in major inaccuracies.
JP 2003-207329 applies APR to find joints and elbows in pipes based on reflection travel time and waveform. The TX is placed at one end of the pipe, with RX in a side pipe not far from TX. The joints are far-enough apart so that reflections do not overlap, and there is no calibration of the TX pulse shape or loudspeaker impulse response, no deconvolution and no leak detection.
JP 7-198527 and JP 7-198528 apply APR to find gas leaks in a supply pipe to a household gas system. TX and RX are near each other at the inlet port of the gas meter. The method compares the “normal” (nominal or calibrated) response of the complete pipe system to measurements taken when the system is being tested. JP 11-125623 discloses an APR system with TX and RX at the same end of a pipe. The state objective of this patent is to detect (unspecified) types of joints or “troubles”. The method uses either frequency sweep or pseudo-noise measurements. The frequency response of the reflections is compared to a library of previous measurements of the joints that system intends to detect. There is no calibration of the loudspeaker impulse, nor mention of deconvolution. The system can detect only objects that have been measured previously and stored in memory.
A common problem in APR systems is the presence of background noise, especially when such measurements are carried out in the field, as opposed to ideal laboratory conditions. This problem is discussed in most academic publications on the subject. Several methods have been proposed in the literature to improve the Signal to Noise Ratio (SNR). One method is to carry out tens or hundreds of measurements successively and average them [Amir1]. Incoherent background noise is reduced considerably this way, though this method prolongs the measurement process to an extent that is unacceptable in certain setups. Other methods involve the use of pseudo-noise signals [Forbes et al. Acta Acustica Vol. 89, pages 743-753, 2003] or frequency sweeps, from which the impulse response can be extracted mathematically. Both methods require much shorter measurement times and are therefore implemented in the proposed system. Thus, it should be understood that an APR system does not necessarily use real pulses but can also use pseudo noise or frequency sweeps. Hereinafter, “APR” is meant to include all types of pulses.
Presently, a major drawback in implementing APR is the presence of long tubes (L1 and L2 in FIG. 1) on either side of the measurement microphone. These cause the instrument to be extremely bulky, even when they are coiled. They also introduce a large degree of attenuation, which limits the accuracy and the range of the instrument. These tubes are the simplest means to prevent the excitation pulse and the reflections from overlapping at the microphone, by creating time delays that prevent this overlap. On the other hand, propagation through these tubes causes attenuation of high frequencies, thereby reducing the bandwidth of the pulse impinging on the object, and reducing the effective range of the equipment. A method to reduce the length of the tubing on only one side of the microphone has been published recently [A. Li, D. B. Sharp and B. J. Forbes, Proc. of the International Symposium on Musical Acoustics, Perugia, Italy, 8-14 Sep. 2001; pp. 391-394].
Separation of overlapping pulses in APR using short tubes on both sides of the microphone has been attempted before, without success [Amir1]. The method requires:                1. Prior measurement of two values:                    a. The excitation pulse emitted by the loudspeaker, P1             b. The acoustic impulse response of the excitation loudspeaker to an impinging pulse Hi.                        2. Applying an algorithm to separate the impulse response of the object from the overlapping measurements. This is based on applying the following formula:        
                              H          s                =                                            Z              i                        -            1                                                              Z                i                            ⁢                              H                i                                      +            1                                              (        1        )            
where:
HsThe impulse response of the object (transformedto the freq. domain) This is the value seeked.ZiThe impulse response of the entire system,including the overlapping reflections.Mathematically Zi = PM/P1, where PMis the raw measurement of the system, andP1 is the measured excitation pulse. Thisis a measured valueHiThe impulse response of loudspeaker, obtained fromthe calibration process (transformed to the freq.domain). This is a value that must be obtainedthrough calibration measurements.Errors in accurately deriving Zi and Hi and various numerical sensitivities in applying the above formula caused this method to give poor results.
Once accurate measurement data is obtained, it is important to perform correct interpretation of this data in order to detect faults, and find their type and location if these are present. Existing methods found in academic literature or patents are based on several techniques. The first is peak detection. Strong reflections arriving before the reflections expected from the end of the pipe indicate discontinuities, though they provide very little information as to their character. In complicated systems where there are valid discontinuities such as changes in cross sections, finding the peaks related only to faults can be difficult and unreliable. A slightly more advanced method is based on comparison to previous measurements of faults. This method is also simplistic, since different sized leaks will have different acoustic patterns, and it may not be feasible to store a large number of such patterns. Furthermore, acoustic wave propagation properties change with temperature and moisture, so that library measurements may not fit well with field measurements taken under varied conditions.
A more general method for fault detection is to apply the general bore reconstruction method [see e.g. V. Chilekawa, D. B. Sharp, T. J. W. Hill, Proc. of the Stockholm Music Acoustics Conference, Stockholm, Sweden, Aug. 6-9, 2003 (hereinafter “Chilekawa”); D. B. Sharp and D. M. Campbell, Acustica 83, 560-566, 1997]. This method is most suited to the detection of obstruction and blockages, since it breaks down in the case of leaks. This method is also sensitive to low frequency noise, when present. Bore reconstruction has been applied to detection of leaks, by taking advantage of the fact that it breaks down in their presence (Chilekawa). As shown in the latter reference, this method is most useful if separate measurements can be taken from either side of the tube, which is rarely feasible. Otherwise, application of the bore reconstruction algorithm gives a false indication of a steadily increasing flare, which can be interpreted as a leak if a priori knowledge indicates that such a flare is not in fact present. Automated detection of such a false flare is not straightforward [Chilekawa], especially when it is located near other discontinuities in the tube.
In summary, to be useful, equipment based on APR should have the following features:                1. Short measurement time        2. High robustness to noise        3. Low bulk        4. Easy and accurate calibration methods        5. Robust and accurate fault detection methods, that do not require previous measurements of each type of fault        
No prior art APR method and system provide all of these features. It is thus desirable to have an APR technology that can provide satisfactory answers to the problems outlined above.